Deciphering the pharmacological mechanisms of Chaenomeles Fructus against rheumatoid arthritis by integrating network pharmacology and experimental validation

Abstract Chaenomeles Fructus is a plant that can be used for both food and medicine. Modern studies have shown that Chaenomeles Fructus has anti‐inflammatory and immunosuppressive effects on arthritis. However, the mechanism of action of Chaenomeles Fructus on rheumatoid arthritis (RA) and its main active ingredients are still unclear. This study was aimed at devising an integrated strategy for investigating the bioactivity constituents and possible pharmacological mechanisms of Chaenomeles Fructus against RA. The components of Chaenomeles Fructus were analyzed using UPLC‐Q‐Exactive orbitrap MS techniques and applied to screen the active components of Chaenomeles Fructus according to their oral bioavailability and drug‐likeness index. Then, we speculated on the potential molecular mechanisms of Chaenomeles Fructus against RA through a network pharmacology analysis. Finally, the potential molecular mechanisms of Chaenomeles Fructus against RA were validated in a complete Freund's adjuvant (CFA)‐induced RA rat model. We identified 48 components in Chaenomeles Fructus and screened seven bioactive ingredients. The results of the network pharmacology prediction and the experimental verification results were analyzed by Venn analysis, and the experimental results concluded that Chaenomeles Fructus mainly interferes with the inflammation of RA by inhibiting arachidonic acid metabolism and the MAPK signaling pathway. This study identified the ingredients of Chaenomeles Fructus by UPLC‐Q‐Exactive orbitrap MS and explained the possible mechanisms of Chaenomeles Fructus against RA by integrating network pharmacology and experimental validation.

Some of the main constituents in Chaenomeles Fructus, including oleanolic acid, betulinic acid, and ursolic acid, possess potential antiinflammatory properties (Zhang et al., 2014). The anti-inflammatory effect of Chaenomeles Frctus is the holistic effect of the combination of its multiple components. However, the mechanism of action of Chaenomeles Fructus on RA and the main active ingredients are still unclear.
Rheumatoid arthritis (RA) is a chronic autoimmune joint disease characterized by inflammation of synovial tissue, which can cause cartilage and bone damage in addition to disability (Smolen et al., 2016). It affects approximately 1% of the population worldwide, and its current treatment strategies are costly (Silman & Pearson, 2002). Moreover, in such disorders, inflammation can further extend to damage other body organs than the joints, comprising the heart, lungs, eyes, and skin (Cojocaru et al., 2010). Currently, medications for the treatment of RA, such as nonsteroidal antiinflammatory drugs (NSAIDs) and disease-modifying antirheumatic drugs (DMARDs), have serious side effects, including cardiovascular diseases and hepatotoxicity, which limit their extensive clinical use . Given the side effects of existing therapies, such as limited efficacy, potential toxicity, and high cost, many countries have paid great attention to herbal therapy (Kuwana et al., 2018). For example, Tripterygium glycosides have anti-inflammatory and immunomodulatory effects and are considered to be the most effective medicinal plants for treating RA in China. However, the side effects and toxicity of Tripterygium glycosides cannot be ignored. Thus, it is necessary to find drugs with good curative effects and few side effects.
With the rise of systems biology, network pharmacology uses big data to visualize the connections of complex systems and to provide new ideas and approaches for the study of mechanisms in treating diseases (Wang et al., 2021). It uses a variety of analytical tools to extract relevant data from massive amounts of biological information and medicinal plant information to build disease gene or medicinal plant active ingredient-protein target interaction networks for data mining, thereby establishing the disease regulatory networks of medicinal plants and their formulas. The synergistic mechanisms of the complicated medicinal plant formulas can be elucidated in greater depth by combining the results from proteomics, transcriptomics, or metabolomics. For instance, in a recent study, the mechanisms of Shenyan Kangfu tablets in treating diabetic nephropathy were studied through network pharmacology combined with metabolomics (Wang et al., 2021).
This study, based on the scientific strategy of network pharmacology, aimed to systematically investigate the predicted therapeutic targets and biological signaling pathways of Chaenomeles Fructus against RA. In addition, we established a complete Freund's adjuvant (CFA)-induced RA rat model for verification. A metabolomics method based on UPLC-Q-Exactive orbitrap MS was used to collect the serum metabolic profiles of rats and explore the metabolic changes that occurred after Chaenomeles Fructus treatment. The MAPK signaling pathway involved in MAPK3 targets was selected for validation in the current models. The specific experimental process is shown in Figure 1.

| Sample preparation
The Chaenomeles Fructus was ground into powder, accurately weighed 0.5 g, dissolved in methanol with sonication for 30 min, and then use methanol to supplement the weight loss. The solution was further filtrated through 0.22 μm membrane for LC-MS/MS analysis.

| Preparation of standard solution
Precisely weigh 2.00 mg of the above-mentioned standard product and place it in a 10 ml volumetric flask. Dissolve to a fixed volume with methanol as the standard solution and store it at 4°C. The mixed solution of the standard was diluted 100 times with methanol and treated with ultrasound at room temperature for 30 min.
Methanol was added to make up the weight loss and filtered through 0.22 μm membrane.

| Network pharmacology study
The construction of the network was mainly based on the overall prediction of the TCMSP (http://ibts.hkbu.edu.hk/lsp/tcmsp. php), Swiss TargetPrediction (http://www.swiss targe tpred iction. ch/), TTD (https://db.idrbl ab.org/ttd/), OMIM (http://omim.org/), F I G U R E 1 Flowchart of the whole study including all the groups. The first level (orange box) is the identification process of Chaenomeles Fructus components; the second level (gray box) is the network pharmacology prediction process; the third level (blue box) is the experimental verification part, including metabolomics and related protein detection STRING (https://strin g-db.org/), and Metascape (http://metas cape. org/) databases for the treatment of RA with Chaenomeles Fructus.
The procedure for network construction was as follows: (1) Based on the qualitative identification results of mass spectrometry, compounds that met the requirements of oral bioavailability (OB) ≥ 30% and drug-likeness (DL) ≥ 0.18 were extracted from the TCMSP database, and the potential active compounds were screened out.
Their corresponding targets were queried in the TCMSP and Swiss TargetPrediction databases (Daina et al., 2019;Ru et al., 2014). (2) We collected gene targets for RA from two sources. The first source was the TTD (http://db.idrbl ab.net/ttd/) (Wang, Yu, et al., 2020). We used the keyword "rheumatoid arthritis" to search this database. The second source was the Online Mendelian Inheritance in Man (OMIM) database (www.omim.org/, updated on 28 February 2019) (Hamosh et al., 2005).
(3) First, we intersected the obtained drug targets with the genes associated with disease and obtained a Venn diagram of the intersected gene symbols. These overlapping targets were further checked and retrieved into UniProt ID by using UniProt (https:// www.unipr ot.org/) (The UniProt Consortium, 2018). (4) The proteinprotein interaction (PPI) analysis was performed by employing String (https://strin g-db.org/) (Szklarczyk et al., 2019) and visualized by Cytoscape 3.8.2 (Shannon, 2003). The action targets of Chaenomeles Fructus on RA ulcers were uploaded from the Metascape (http:// metas cape.org/) database, and the functions of biological process (BP), cellular component (CC), and molecular function (MF) were obtained by enrichment, and an enrichment analysis was carried out (Zhou, Yu, et al., 2019). Through the analysis of KEGG signaling pathways in the Metascape database, we comprehensively predicted the biological characteristics and regulatory pathways of Chaenomeles Fructus acting on RA targets. The calculation formula (RichFather = the number of genes belonging to this pathway in the target gene set/ the number of all genes in this pathway in the background gene set) was adopted, and the bubble chart was drawn .

| Animals
Male Sprague-Dawley (SD) rats (weight, 160-200 g) were purchased from Beijing Weitong Lihua Experimental Animal Technology Co., Ltd (Beijing, China;Certificate No. SCXK 2016-0006). All rats were housed in a specific pathogen-free (SPF) facility at a constant temperature of 23° ± 1°C with a relative humidity environment of 55% ± 5% and a standard 12 h/12 h (light/dark) cycle. Animals were allowed free access to water and fed a unified basic diet. Prior to the start of the experiment, the animals were maintained in hygienic conditions for at least a week to adapt to the environment.

| Animal experiments
Animals were randomly divided into six groups (11 rats/group). Each rat was injected with 0.1 ml CFA (10 mg/ml) both in and around the articular cavity, except for the control group. Treatment of rats began 1 day after induction. Group 1 included nonimmunized rats (control), and rats in Groups 2-6 included animals receiving the experimental drug. Group 2 included rats treated with intragastric saline administration (model). Group 3 included rats treated with intragastric Chaenomeles Fructus at 0.15 g/kg/day (low). Group 4 included rats treated with intragastric Chaenomeles Fructus at 0.30 g/kg/day (medium). Group 5 included rats treated with intragastric Chaenomeles Fructus at 0.60 g/kg/day (high). Group 6 included rats treated with intragastric TG at 0.009 g/kg/day (TG). Rats received administration for 3 weeks. After 24 h of the last administration, all animals were anesthetized with 1.5% pentobarbital sodium, and blood samples and synovial tissue were collected. Blood samples were drawn into the Eppendorf tubes, allowed to clot for 30 min, and then centrifuged (999 g, 4°C) for 15 min to obtain serum samples. The serum samples and synovial tissue were stored at −80°C until analysis.

Preparation of serum samples
When the serum metabolites were analyzed, the serum samples were melted at 4°C. Serum samples (100 μl) and acetonitrile (400 μl) were mixed in a tube to remove proteins from the serum, including 2-chloro-L-phenylalanine (0.05 mg/ml, 15 μl) as an internal standard.
The mixture was vortexed for 2 min, allowed to stand at 4°C for 10 min, and then centrifuged at 15,984 g for 20 min at 4°C. The supernatant (400 μl) was placed in a 2 ml EP tube, dried with nitrogen, and then redissolved by adding the initial mobile phase of 100 μl. The solution was centrifuged at 12,000 rpm for 5 min at 4°C, and 70 μl of the supernatant was injected into the column for LC-MS analysis.
2.4.5 | Western blot to detect the protein expression of ERK, JNK, P-ERK, and P-JNK A protein extraction kit was used to extract the total protein from synovial tissue, and a BCA kit was used to detect the protein concentration of the sample. Each group took a sample solution containing the same total protein mass for electrophoresis, transferred the protein to a PVDF membrane, added 5% skimmed milk powder, and blocked for 2 h at room temperature. The dilution multiples of rabbit ERK, JNK, P-ERK, and P-JNK primary antibodies were 1:1000, 1:1000, 1:1000, and 1:2000, respectively. The goat anti-rabbit secondary antibody was diluted at 1:5000, and the color was developed by enhanced chemiluminescence. The gel imaging system was used to take pictures, and the relative expression of each protein group was analyzed by ImageJ software.

| Data processing and statistical analysis
Data were collected by using the Xcalibur data system that comes with the instrument. The acquired mass spectrometry data (.raw) were exported into Compound Discover (CD, Thermo Fisher, CA, USA) software for data analysis. CD software converts mass spectrometry data into metabolite information. These metabolic discoveries are achieved through a combination of online open databases and local databases, and MS/MS data of metabolites, which greatly improves the accuracy of metabolite identification. To find the differences between the groups, the data were imported into SIMCA-P13.0 software (Umetrics, Sweden) for principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), using PLS-DA for supervised pattern recognition analysis of serum data. Through the CD software, the variables with p < .05 and VIP values >1 were screened as difference variables, and the metabolites with mzCloud matching higher than 70 were screened as potential biomarkers. The exact mass of potential biomarkers was searched in databases such as HMDB (http://www. hmdb.ca), METLIN (https://metlin.scrip ps.edu), and KEGG (http:// www.genome.jp/kegg/) for biomarker identification. The identified biomarkers were introduced into metaboanalyst 4.0 (http://www. metab oanal yst.ca) for metabolic pathway analysis, and those whose critical value of metabolic pathway impact was greater than 0.01 were selected as a key potential metabolic pathway to integrate the metabolic pathway and carry out metabolic network analysis to find the core target of Chaenomeles Fructus intervention.
Statistical analysis was performed using SPSS 17.0 software, and the experimental data are expressed as the mean ± standard deviation ( − X ± SD). T tests were used to compare two groups, and the one-way analysis of variance (ANOVA) was used to compare differences between multiple groups. p < .05 was considered to indicate a significant difference.

| Identification of chemical components in Chaenomeles Fructus
Chaenomeles Fructus samples were analyzed under the section "UPLC-Q-Exactive Orbitrap MS for Chemical Characterization" chromatographic and mass spectrometry conditions to obtain the UPLC-Q-Exactive orbitrap MS total ion current diagram of Chaenomeles Fructus, as shown in Figure 2. The results are shown in Table S1.

| Organic acids
In negative-ion mode, the retention time of organic acids is 0.71

| Network construction
Using the 48 identified compounds to find their targets in TCMSP and Swiss TargetPrediction, a total of 316 targets were obtained and named according to their gene symbols, and a total of 7 compounds were screened (Table 1). We used "rheumatoid arthritis" as the search term to construct related targets of RA in the TTD and OMIM databases and then merged the gene targets retrieved from the two databases, removed duplicate values, and took their union. A total of 368 related genes were identified. The Venny 2.1.0 tool (https:// bioin fogp.cnb.csic.es/tools/ venny/) was used to map and compare the target genes of Chaenomeles Fructus with the RA genes to obtain the intersection of 77 target genes ( Figure 3a). Then, they were imported into the STRING database and Cytoscape 3.7.1 to construct a PPI network to obtain protein interaction relationships (Figure 3b).
The result of the screening degree score greater than 21 shows that there are 25 nodes and 238 edges in the network, and TNF, IL6, IL1B, VEGFA, MAPK3, etc., are its main targets. GO function annotation includes three aspects: cell component, molecular function, and biological process. Cell components mainly involved the side of the membrane, apical part of the cell, and secretory granule lumen; molecular function was mainly coupled with oxidoreductase activity, monocarboxylic acid binding, and cytokine receptor binding; and the biological process mainly involved the icosanoid metabolic process, steroid metabolic process, response to lipopolysaccharide, and regulation of inflammatory response. (Figure 3c). The top 20 significantly enriched pathways are shown in Figure 3d. Among these potential pathways, the AGE-RAGE signaling pathway, cAMP signaling pathway, HIF-1 signaling pathway, and arachidonic acid metabolism were also included, which were categorized as related to inflammation. After integrating drug target prediction, pathway and function enrichment, and network analyses, we identified TNF, IL6, IL1B, VEGFA, and MAPK3 as relatively highly relevant targets in inflammation. Additionally, they are considered the key targets of Chaenomeles Fructus in the treatment of RA. Thus, we speculated that Chaenomeles Fructus may interfere with inflammation

| CFA-injected rats
During the experiment, it was observed that the rats were easy to move, and the sizes of the left and right feet were similar before modeling. After modeling for 4 h, the rats developed swelling of the right toe and limited movement, especially the arthritic symptoms of the right foot, which indicated that the model was successful. There was no significant change in the volume of the toes of the rats in the control group, and the rats were active.
The swelling of the right foot of the rats in each dose group of Chaenomeles Fructus and TG group was significantly relieved after administration (Figure 4). Compared with the model group, the swelling degree of the rat toes in the high-and medium-dose groups was significantly reduced after 21 days.

| Multivariate statistical analysis of metabolomics data
All quality control (QC) samples were pooled together and concentrated at a 95% confidence interval, which indicates that the instrument is working properly and that the data quality is reliable. The metabolites were discovered with significant differences between the groups shown by PCA (Figure 5a), PLS-DA (Figure 5b), and S-plot  Table S2.

| Metabolic pathway analysis
The differentially produced metabolites were entered into Metaboanalyst (http://www.metab oanal yst.ca) for enrichment analysis of the metabolic pathways. Metabolic pathway analysis found that Chaenomeles Fructus mainly interferes with arachidonic acid metabolism, nicotinate and nicotinamide metabolism, tryptophan metabolism, and branched-chain amino acid catabolism (valine, leucine, and isoleucine degradation) ( Figure 6).

| Effect of Chaenomeles
Fructus alcohol extract on the protein expression of ERK, JNK, P-ERK, and P-JNK in the synovium of RA model rats The protein expression results are shown in Figure 7 and Table 2.
The expression of ERK, JNK, P-ERK, and P-JNK proteins related to inflammation increased, but their expression levels showed significant differences compared with the control group (p < 0.01). After the intervention, the expression levels of ERK, JNK, and P-JNK decreased, which were significantly different from the model group (p < 0.01).

| DISCUSS ION
In recent years, the application of liquid-mass spectrometry and the success rate of modeling is high Li, Zhang, et al., 2021). The CIA modeling process is cumbersome, and the preparation of the immune mixture requires the entire operation on ice . The economic cost of SCW model inducers is relatively high, and the intra-articular injection technique is difficult. The peptidoglycan-polysaccharide (PG-PS) structure of the different Streptococcus cell walls and the degree of development of the SCW are also different, which leads to the frequency of use of the SCW model in the study being low (Cromartie et al., 1977;Marijnissen et al., 2014). The course of PIA is less invasive, but the construction of the model is time-consuming (Hutamekalin et al., 2009;Song et al., 2015). In this study, the CFA-induced AA and is of great significance to the formation of chronic inflammation. The serum VEGF level is an important indicator for judging the condition and prognosis of RA (Yu et al., 2018). MAPK3, known as ERK1, is involved in many pathogenic processes of RA, including its role in promoting the expression of inflammatory cytokines (Bauer et al., 2000). Combined with GO function and KEGG pathway enrichment analysis, the intersection target mainly involves the AGE-RAGE signaling pathway, cAMP signaling pathway, HIF-1 signaling pathway, arachidonic acid metabolism, and other inflammatory response regulation pathways. Studies have shown that AGE-RAGE can stimulate the production of proinflammatory factors. It can also act as an inflammatory factor to activate innate immune cells and further lead to the development of arthritis (Millerand et al., 2019). As an important second messenger in cells, cAMP's main role is to bind to PKA regulatory subunits, thereby activating PKA. The increase in the content of cAMP and the activation of PKA eventually cause the activation of NF-κB and the increase in the expression of the proinflammatory cytokine IL-6, which are thought to be associated with RA disease progression (Ilchovska & Barrow, 2021;Narasimamurthy et al., 2012;Wu et al., 2013). HIF-1α is a very important transcriptional regulator in the mammalian body under hypoxic conditions.
Studies have shown that NF-κB can promote the secretion of inflammatory factors by macrophages in a HIF-1α-dependent and HIF-1α-independent manner, thereby inducing the occurrence of RA (Knowles et al., 2006). The arachidonic acid (AA) metabolic pathway is an important metabolic pathway in the inflammatory response and is highly activated in the inflammatory response.
When cells are stimulated, the cell membrane phospholipase A2 (PLA2) releases AA (Yu et al., 2022). The metabolite of arachidonic acid, prostaglandin E2 (PGE2), is a major mediator of inflammation in diseases such as rheumatoid arthritis and osteoarthritis (Park et al., 2006). It can be seen that Chaenomeles Fructus mainly alleviates RA from an anti-inflammatory perspective.
There are many signaling pathways involved in inflammation, and the inhibition of the MAPK signaling pathway is an important means to effectively control the occurrence of inflammation. MAPK mainly includes three major pathways: JNK, ERK1/2, and p38. JNK can be activated by inflammatory factors such as TNFα and IL-1β. ERK1/2 can promote the expression of inflammatory cytokines, and p38 can promote the expression and secretion of inflammatory factors in cells (Bauer et al., 2000;Luo et al., 2022). and an important mediator that regulates inflammation (Higgins & Lees, 1984;Sala et al., 2018). It is involved in the metabolism of arachidonic acid and the synthesis and release of inflammatory factors such as tumor factors and interleukins (Lewis et al., 1990).
BCAAs include leucine, isoleucine, and valine. Supplementation with BCAAs could activate mTORC1 and upregulate the NF-κB signaling pathway, increasing the release of proinflammatory cytokines in human peripheral blood mononuclear cells and endothelial cells (Ye et al., 2020;Zhenyukh et al., 2017Zhenyukh et al., , 2018

CO N FLI C T O F I NTE R E S T
The authors declare that there are no conflicts of interest.

E TH I C S A PPROVA L A N D CO N S E NT TO PA RTI CI PATE
All animal welfare and experimental procedures were performed in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals, and the protocols used were approved by the Animal Ethics Committee of Shandong University of Traditional Chinese Medicine Laboratory Animal Center, Jinan, China.

DATA AVA I L A B I L I T Y S TAT E M E N T
The data that support the findings of this study are available from the corresponding author upon reasonable request.

S U PP O RTI N G I N FO R M ATI O N
Additional supporting information may be found in the online version of the article at the publisher's website.